Watermarking techniques in stream processing serve to track and manage the progress of event processing over time. In a streaming system, data flows continuously, and events can arrive at different times due to factors like network latency or varying producer speeds. A watermark is a special marker inserted into the stream that indicates the point in time up to which all previous events have been processed. This helps the system understand the completeness of the data being handled and guides it in making decisions about when to trigger computations or handle late-arriving events.
There are two primary types of watermarks: bounded and unbounded. A bounded watermark signifies that no events with timestamps earlier than the watermark will be processed subsequently. For example, if a stream processes data with timestamps, and a watermark is emitted for time t=10, it means all events with timestamps <=10 have been fully processed. Unbounded watermarks, on the other hand, indicate that the system is uncertain about late arrivals; it continues to allow for some flexibility in handling late events for a while, often guarding against the possibility of missing important data.
Using watermarks is essential for ensuring that stream processing maintains correctness and efficiency. For instance, in scenarios like windowed aggregations, where events are grouped over time intervals, watermarks help close windows and emit results based on the latest events processed. Without watermarks, systems could either process events repeatedly or miss important ones, leading to incorrect results. In practical implementations, tools and frameworks like Apache Flink utilize watermarks to maintain event order and ensure timely processing, allowing developers to streamline their applications with reliable data handling.